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Introduction to Statistical and Machine Learning Methods for Data Science

Release Date: 2021/08/01
ISBN: 9781953329622
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Boost your understanding of data science techniques to solve real-world problems

Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need.

No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.

Table of Contents

  1. About This Book
  2. About These Authors
  3. Acknowledgments
  4. Foreword
  5. Chapter 1: Introduction to Data Science
    1. Chapter Overview
    2. Data Science
    3. Mathematics and Statistics
    4. Computer Science
    5. Domain Knowledge
    6. Communication and Visualization
    7. Hard and Soft Skills
    8. Data Science Applications
    9. Data Science Lifecycle and the Maturity Framework
    10. Understand the Question
    11. Collect the Data
    12. Explore the Data
    13. Model the Data
    14. Provide an Answer
    15. Advanced Analytics in Data Science
    16. Data Science Practical Examples
    17. Customer Experience
    18. Revenue Optimization
    19. Network Analytics
    20. Data Monetization
    21. Summary
    22. Additional Reading
  6. Chapter 2: Data Exploration and Preparation
    1. Chapter Overview
    2. Introduction to Data Exploration
    3. Nonlinearity
    4. High Cardinality
    5. Unstructured Data
    6. Sparse Data
    7. Outliers
    8. Mis-scaled Input Variables
    9. Introduction to Data Preparation
    10. Representative Sampling
    11. Event-based Sampling
    12. Partitioning
    13. Imputation
    14. Replacement
    15. Transformation
    16. Feature Extraction
    17. Feature Selection
    18. Model Selection
    19. Model Generalization
    20. Bias–Variance Tradeoff
    21. Summary
  7. Chapter 3: Supervised Models – Statistical Approach
    1. Chapter Overview
    2. Classification and Estimation
    3. Linear Regression
    4. Use Case: Customer Value
    5. Logistic Regression
    6. Use Case: Collecting Predictive Model
    7. Decision Tree
    8. Use Case: Subscription Fraud
    9. Summary
  8. Chapter 4: Supervised Models – Machine Learning Approach
    1. Chapter Overview
    2. Supervised Machine Learning Models
    3. Ensemble of Trees
    4. Random Forest
    5. Gradient Boosting
    6. Use Case: Usage Fraud
    7. Neural Network
    8. Use Case: Bad Debt
    9. Summary
  9. Chapter 5: Advanced Topics in Supervised Models
    1. Chapter Overview
    2. Advanced Machine Learning Models and Methods
    3. Support Vector Machines
    4. Use Case: Fraud in Prepaid Subscribers
    5. Factorization Machines
    6. Use Case: Recommender Systems Based on Customer Ratings in Retail
    7. Ensemble Models
    8. Use Case Study: Churn Model for Telecommunications
    9. Two-stage Models
    10. Use Case: Anti-attrition
    11. Summary
    12. Additional Reading
  10. Chapter 6: Unsupervised Models—Structured Data
    1. Chapter Overview
    2. Clustering
    3. Hierarchical Clustering
    4. Use Case: Product Segmentation
    5. Centroid-based Clustering (k-means Clustering)
    6. Use Case: Customer Segmentation
    7. Self-organizing Maps
    8. Use Case Study: Insolvent Behavior
    9. Cluster Evaluation
    10. Cluster Profiling
    11. Additional Topics
    12. Summary
    13. Additional Reading
  11. Chapter 7: Unsupervised Models—Semi Structured Data
    1. Chapter Overview
    2. Association Rules Analysis
    3. Market Basket Analysis
    4. Confidence and Support Measures
    5. Use Case: Product Bundle Example
    6. Expected Confidence and Lift Measures
    7. Association Rules Analysis Evaluation
    8. Use Case: Product Acquisition
    9. Sequence Analysis
    10. Use Case: Next Best Offer
    11. Link Analysis
    12. Use Case: Product Relationships
    13. Path Analysis
    14. Use Case Study: Online Experience
    15. Text Analytics
    16. Use Case Study: Call Center Categorization
    17. Summary
    18. Additional Reading
  12. Chapter 8: Advanced Topics in Unsupervised Models
    1. Chapter Overview
    2. Network Analysis
    3. Network Subgraphs
    4. Network Metrics
    5. Use Case: Social Network Analysis to Reduce Churn in Telecommunications
    6. Network Optimization
    7. Network Algorithms
    8. Use Case: Smart Cities – Improving Commuting Routes
    9. Summary
  13. Chapter 9: Model Assessment and Model Deployment
    1. Chapter Overview
    2. Methods to Evaluate Model Performance
    3. Speed of Training
    4. Speed of Scoring
    5. Business Knowledge
    6. Fit Statistics
    7. Data Splitting
    8. K-fold Cross-validation
    9. Goodness-of-fit Statistics
    10. Confusion Matrix
    11. ROC Curve
    12. Model Evaluation
    13. Model Deployment
    14. Challenger Models
    15. Monitoring
    16. Model Operationalization
    17. Summary
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